A key benefit of open-vocabulary detection is to test on out-of-distribution data with categories given by users on the fly. Thus, we apply F-VLM to Ego4D [1]}, a real-world ego-centric application. We train F-VLM on a mixture of full LVIS, Objects365, and COCO datasets to expand its training vocabulary for application in the wild, and use the R50x16 backbone for this experiment. The model is not trained on Ego4D in order to evaluate for transfer detection. The categories are provided by the user based on visual inspection of the video.
